PM1 steganography in JPEG images using genetic algorithm

Yu, Lifang; Zhao, Yao; Ni, Rongrong; Zhu, Zhenfeng

doi:10.1007/s00500-008-0327-7

Cited by 37 publications

(29 citation statements)

References 13 publications

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“…Due to the lack of a perfect model, this steganographic algorithm can be broken using the first-order statistics [63]. Moreover, it can also be detected by the difference of 'blockiness' between a stego-image and its estimated image reliably [64]. The discovery of 'blockiness' led the author in [58] to produce an enhanced version called MB2, a model-based with de-blocking.…”

Section: Adaptive Steganographymentioning

confidence: 99%

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Digital image steganography: Survey and analysis of current methods

et al. 2010

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Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganography's ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the object-oriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed.

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Section: Adaptive Steganographymentioning

confidence: 99%

“…It should be noted that image hiding using genetic algorithm was known prior to their work such as the work in [75]. In [64], the authors proposed extending the conventional ' 1 ' algorithm to JPEG images using genetic algorithm.…”

Section: Adaptive Steganographymentioning

confidence: 99%

Digital image steganography: Survey and analysis of current methods

et al. 2010

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“…al [45] 2009 Proposed review of different steganographic techniques along with their analysis and further recommended to use object oriented technology for embedding Yu et. al [46] 2008 Used an enhanced version of original LSB approach named Plus Minus 1 (PM1) over JPEG images using genetic algorithms based approach. Proposed method increase capacity as well as security of data.…”

Section: Abdul and Gutub 2010mentioning

confidence: 99%

Steganography techniques: a survey

2017

IJLTET

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“…Due to the lack of a perfect model, this steganographic algorithm can be broken using the first-order statistics [35]. Moreover, it can also be detected by the difference of ‗‗blockiness'' between a stego-image and its estimated image reliably [41]. The discovery of ‗‗blockiness'' led the author in [21] to produce an enhanced version called MB2, a modelbased with de-blocking.…”

Section: Milestones For Adaptive Steganographymentioning

confidence: 99%